Sequential method for speech segmentation based on Random Matrix Theory

Speech segmentation to covariance-stationary regions is of great interest in various fields of speech processing. This study presents a novel sequential segmentation algorithm relying on Random Matrix Theory (RMT). The proposed approach utilises an RMT-derived test statistic checking stationarity of sample covariance matrices. Utilising the statistical properties of the sample eigenvalues recently investigated in the RMT literature, a new expression for the decision threshold is derived in the non-asymptotic case where the number of samples is comparable with the dimension. This derivation is of great help to detect very short non-stationary intervals where the performance of most segmentation methods based on Autoregressive models degrades. Furthermore, based on the results of previous step, a new segmentation procedure is proposed. This procedure is applied to both synthetic and real-world speech data. Comparing the obtained simulation results with the state-of-the-art segmentation algorithms, the proposed approach has demonstrated good performance with extremely lower computational cost.